Epilepsy is the unstable state caused by excessive discharge of brain cells. In more than 30 percent of epilepsy cases, seizures cannot be controlled with medication or surgery. Refractory epilepsy seriously affects the health of patients and brings great economic burden to families. Therefore, this requires an effective seizure classification and prediction method to reduce risk in epilepsy patients. Researchers proposed machine learning or deep learning methods to predict seizures. However, automatic screening of electrode channels and improvement of predictive accuracy remain a challenge. A multi-channel feature fusion model CNN-Bi-LSTM.This method only requires simple preprocessing. CNN is responsible for extracting spatial features, Bi-LSTM is responsible for extracting temporal features, and finally, two channel weights are allocated through the attention mechanism to filter out the results of the more weighted electrode channel output classification. The performance of the model is tested on the CHB-MIT dataset, and the output is divided into three categories, including normal, pre-seizure and mid-seizure. The ten-fold cross-validation average accuracy is 94.83%, the precision is 94.84%, the recall is 94.84%, the F1-score is 94.83%, and the MCC is 92.26% across CHB-MIT EEG.The ten-fold cross-validation average accuracy of UCI data set is 77.62%, the precision is 77.66%, the recall is 77.62%, the F1-score is 77.60%, and the MCC is 72.03%. The results showed that this method is superior to existing methods and can predict the EEG signals of epilepsy in advance. This work will be extended to design a removable epilepsy predictive device for real-time use.

Purpose: To assess the efficacy and safety of rituximab treatment as second-line immunotherapy in pediatric cases of anti-NMDA receptor (NMDAR) encephalitis.Methods: We retrospectively recruited 8 patients with anti-NMDAR encephalitis who were treated with rituximab as second-line immunotherapy. We evaluated the clinical features, laboratory examination results and treatment protocols of the Chinese children and defined good outcomes based on the modified Rankin scale (mRS) score (0–2) at the last follow-up.Results: A total of eight pediatric patients (median age 6.7 years; four female) with refractory anti-NMDAR encephalitis were recruited to the study. Rituximab was given after a median duration of disease of 57 days (range 50.5–113.75 days). The use of rituximab led to a significant reduction in the mRS and CD19+ B-cells compared to before rituximab infusion (P < 0.05). Five patients (62.5%) had a good outcome (mRS ≤ 2) including four patients (50%) who showed complete recovery (mRS = 0) at the last follow-up. Transient infusion adverse events were recorded in 2 patients (25%). Two patients (25%) had severe infectious adverse events (AEs) and two patients with grade 5 (death). None of the patients developed progressive multifocal leukoencephalopathy (PML).Conclusion: Our study provides evidence that rituximab can efficiently improve the clinical symptoms of anti-NMDAR encephalitis in children. However, due to the risk of adverse infections, rituximab should be restricted in pediatric patients with high rates of mortality and disability.

Since sudden and recurrent epileptic seizures seriously affect people’s lives, computer-aided automatic seizure detection is crucial for precise diagnosis and prompt treatment. A novel seizure detection algorithm named channel selection-based temporal convolutional network (CS-TCN) was proposed in this paper. Firstly, EEG recordings were segmented into 2-second intervals and features were extracted from both the time and frequency domains. Then the expanded fisher score channel selection method was employed to select channels that contribute the most to seizure detection. Finally, the features from selected EEG channels were fed into the temporal convolutional network (TCN) to capture inherent temporal dependencies of EEG signals and detect seizure events. CHB-MIT and Siena datasets were used to verify the detection performance of the CS-TCN algorithm, achieving sensitivities of 98.56% and 98.88%, and specificities of 99.80% and 99.88% in sample-wise analysis, respectively. In event-wise analysis, the algorithm achieved sensitivities of 97.57% and 95.00%, with delays of 6.91 and 18.62 seconds, and FDR/h of 0.11 and 0.39, respectively. These results surpassed state-of-the-art few-channel algorithms for both datasets. CS-TCN algorithm offers excellent performance while simplifying model complexity and computational requirements, thus showcasing its potential for facilitating seizure detection in home environments.